Decoding the Algorithm: The Mathematics Behind TikTok’s Short-Form Content Success

Within the realm of social networks, TikTok has become the central hub for short-form video content. The network’s unique ability to capture individual preferences using predictive analytics has greatly contributed to its massive success, allowing the company to optimize its performance and content personalization. In an age where digital media have such a significant influence on society, it is essential that users develop an understanding of how social network algorithms function to make more informed online decisions. Although TikTok’s technological system is primarily undisclosed, the platform certifiably leverages several key mathematical principles within its algorithm to achieve its core goals of generating user, creator, and platform value. This thesis reveals how mathematical modeling is embedded into the application’s design in order to optimize content delivery, personalize the user experience, and ultimately increase engagement and platform growth. Matrix factorization is implemented in the application’s operating system to track video-user interactions and predict engagement. The network uses probability models to estimate the likelihood that a user will engage with a video based on past behavior, observing metrics such as watch time, likes, and shares. With the help of artificial intelligence, machine learning algorithms analyze videos to match user interests and adjust recommendations in real time. Lastly, the use of Fourier transforms allows the company to perform trend analysis on sound waves, predicting the success of music and speech on the platform. Each component of the algorithm ensures that the platform produces high-quality content recommendations that encourage long-term engagement from users.